Any discussion of the background art throughout the specification should in no way be considered as an admission that such art is widely known or forms part of common general knowledge in the field.
Proper detection of eyes in sensed noisy images can be difficult, especially in the presence of glasses with the occlusion generated by their frame and the reflections occurring on the lenses.
Traditional computer vision algorithms for eye detection often rely on appearance (e.g. U.S. Pat. No. 7,020,337 to Viola and Jones entitled “System and method for detecting objects in images”). This method relies on training a model based on the appearance of the object to be detected and its robustness will degrade significantly in the presence of noise such as strong reflections and/or occlusions. Further, this method is relatively computationally intensive.
Another example is the method described in U.S. Pat. No. 7,460,693 to Loy and Thomsen entitled “Method and apparatus for the automatic detection of facial features”. In this document the eyes are detected using a fast symmetry transform (using the circular symmetry of the iris) and then refined using a Hough transform (which detects circles in images). This method relies on the texture of the eyes and its performance will degrade significantly if the iris is partially occluded by specular reflections on the lenses of glasses for example.
In addition, certain prior art systems comprise whole eye detection modules running in parallel. Such systems have inherent disadvantages. For example, in some situations, the eye feature may occupy a significant portion of the image (e.g. for a phone camera held close to the face, it may be that 20% of the pixels will fall on the eye). In these circumstances, the eye detectors will have to operate over areas of the image that overlap by this amount, otherwise the eye will not be detectable (referred to herein as “the overlap problem”). The overlap creates additional redundant processing on the same pixel data and can create multiple detections of the same eye from different detectors which require further processing to disambiguate.
In addition, where the eye detector operates on multiple frames and the eye is moving (creating a trajectory), such prior art systems will not be able to resolve a trajectory that moves across the multiple eye detection regions. Instead it will report multiple trajectories with a discontinuity between them.
Therefore, there is a general need for a more robust form of eye detection in noisy or occluded images.